The common denominator is that all of these EFC techniques were developed for the purpose of either binary or multi-class classification problems. No EFC approach for multi-label classification has been proposed so far. As will be explained in more detail in Section , multi-label classification differs from multi-class classification, as it allows a vector of possible class labels to which a sample belongs (e.g., there can be overlapping or nested groups to which a sample may belong, such as a bull terrier belongs to the classes 'dog' and 'animal', but not to the classes 'cat' or 'ball'). In general, in multi-label problems, there is no constraint as to how many of the (possible) classes to which a sample can be assigned [1]} (also see Section ). In this sense, multi-label classification has become more and more an important factor in the increasing complexity of todays’ classification systems [2]}, typically inducing higher class ambiguities or widening the possible groups of classes to which samples may belong. This is even more the case within an online stream classification context [1]}, for which an evolving fuzzy approach is desirable to meet the challenges discussed in the first two paragraphs above.
